First, we load, filter, and merge the data sets.
How does the data set looks like
Applied tresholds are indicated by grey horizontal line.
#Apply tresholds
data <- subset(data, Mean_Puncta_mitoTracker_AreaShape_Area < 200)
data <- subset(data, Mean_Puncta_mitoTracker_Number_Object_Number < 1200)
data <- subset(data, mitoTracker_MeanArea < 0.04)
data <- subset(data, mitoTracker_MeanCount < 0.2)
data <- subset(data, mitoTracker_MeanLength < 0.1)
data <- subset(data, Branchpoints < 200)
#Save data set
#write.csv(data, file = "results/tables/data_mitoTracker.csv")
Cell counts per cell line:
#data <- read.csv("results/tables/data_mitoTracker.csv")
table(data$Metadata_SampleID)
##
## i1JF-R1-018 iG3G-R1-039 i1E4-R1-003 iO3H-R1-005 i82A-R1-002 iJ2C-R1-015
## 142 283 165 187 101 188
## iM89-R1-005 iC99-R1-007 iR66-R1-007 iAY6-R1-003 iPX7-R1-001 i88H-R1-002
## 107 122 94 204 192 111
Mean cell count:
mean(table(data$Metadata_SampleID))
## [1] 158
Various mitochondrial parameters are visualized for each patient-derived cell line as well as for the disease state Mean Ctrl levels are indicated by grey horizontal line.
Nested approach (“Mitochondrial Parameter” ~ Disease_state + (1 | Disease_state:Metadata_SampleID)) to compensate for dependencies within the groups.
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Mean_Puncta_mitoTracker_AreaShape_Area ~ Disease_state + (1 |
## Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 18615.6 18637.8 -9303.8 18607.6 1892
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8287 -0.7395 -0.2055 0.5161 4.5521
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 46.74 6.837
## Residual 1057.22 32.515
## Number of obs: 1896, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 62.1740 3.2679 19.026
## Disease_statesPD 0.5875 4.2987 0.137
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.760
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Mean_Puncta_mitoTracker_AreaShape_Area
## Chisq Df Pr(>Chisq)
## Disease_state 0.0187 1 0.8913
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Mean_Puncta_mitoTracker_Number_Object_Number ~ Disease_state +
## (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 25346.7 25368.9 -12669.4 25338.7 1892
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3961 -0.7283 -0.1411 0.5909 4.1143
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 5763 75.92
## Residual 36539 191.15
## Number of obs: 1896, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 316.77 34.63 9.148
## Disease_statesPD -49.53 45.40 -1.091
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.763
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Mean_Puncta_mitoTracker_Number_Object_Number
## Chisq Df Pr(>Chisq)
## Disease_state 1.1899 1 0.2754
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Mean_Puncta_mitoTracker_Intensity_MeanIntensity_Corr_mitoTracker ~
## Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -2314.6 -2292.5 1161.3 -2322.6 1892
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2119 -0.7355 -0.0701 0.6921 3.1743
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 0.00478 0.06914
## Residual 0.01679 0.12959
## Number of obs: 1896, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.31221 0.03126 9.987
## Disease_statesPD -0.03099 0.04097 -0.756
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.763
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Mean_Puncta_mitoTracker_Intensity_MeanIntensity_Corr_mitoTracker
## Chisq Df Pr(>Chisq)
## Disease_state 0.5722 1 0.4494
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## mitoTracker_MeanArea ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -13359.2 -13337.0 6683.6 -13367.2 1892
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2377 -0.6945 -0.2883 0.3382 4.3343
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 2.935e-07 0.0005418
## Residual 5.057e-05 0.0071114
## Number of obs: 1896, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 9.432e-03 3.459e-04 27.265
## Disease_statesPD 6.776e-06 4.619e-04 0.015
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.749
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: mitoTracker_MeanArea
## Chisq Df Pr(>Chisq)
## Disease_state 2e-04 1 0.9883
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## mitoTracker_MeanCount ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -6859.1 -6836.9 3433.6 -6867.1 1892
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7884 -0.6770 -0.2753 0.4021 3.9171
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 0.0001442 0.01201
## Residual 0.0015385 0.03922
## Number of obs: 1896, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.049048 0.005549 8.839
## Disease_statesPD -0.007567 0.007282 -1.039
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.762
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: mitoTracker_MeanCount
## Chisq Df Pr(>Chisq)
## Disease_state 1.0796 1 0.2988
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## ObjectSkeleton_NumberBranchEnds_mitoTracker_Skeleton ~ Disease_state +
## (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 13175.6 13197.8 -6583.8 13167.6 1892
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4830 -0.7417 -0.1913 0.5159 6.1339
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 2.18 1.476
## Residual 60.05 7.750
## Number of obs: 1896, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 9.3058 0.7151 13.014
## Disease_statesPD -0.4871 0.9414 -0.517
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.760
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ObjectSkeleton_NumberBranchEnds_mitoTracker_Skeleton
## Chisq Df Pr(>Chisq)
## Disease_state 0.2677 1 0.6049
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Branchpoints ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 17632.2 17654.4 -8812.1 17624.2 1892
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4529 -0.7001 -0.2597 0.4125 6.5992
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 35.15 5.928
## Residual 628.55 25.071
## Number of obs: 1896, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 25.2258 2.7968 9.020
## Disease_statesPD 0.3622 3.6757 0.099
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.761
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Branchpoints
## Chisq Df Pr(>Chisq)
## Disease_state 0.0097 1 0.9215
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: ObjectSkeleton_TotalObjectSkeletonLength_mitoTracker_Skeleton ~
## Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 23903.0 23925.2 -11947.5 23895.0 1892
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3610 -0.7132 -0.3259 0.4500 6.2452
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 716.5 26.77
## Residual 17195.4 131.13
## Number of obs: 1896, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 131.885 12.842 10.269
## Disease_statesPD -7.139 16.897 -0.423
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.760
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ObjectSkeleton_TotalObjectSkeletonLength_mitoTracker_Skeleton
## Chisq Df Pr(>Chisq)
## Disease_state 0.1785 1 0.6727
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## mitoTracker_MeanLength ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -10348.4 -10326.2 5178.2 -10356.4 1892
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3092 -0.7531 -0.2992 0.4391 4.9596
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 6.093e-06 0.002468
## Residual 2.460e-04 0.015686
## Number of obs: 1896, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.016884 0.001235 13.670
## Disease_statesPD -0.001501 0.001629 -0.921
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.758
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: mitoTracker_MeanLength
## Chisq Df Pr(>Chisq)
## Disease_state 0.8484 1 0.357